In this article, we would like to present a machine learning algorithm that processes the data of Járó’s target stands and their growth for each forest site variation. The method is able to propose stand types and growths on the basis of existing data for new variations due to climate change and for a newly entering forest climate zone. The essence of this process is to place the entries of the Járó’s table in a five-dimensional space, and use distance kernels to select the closest target stand types and weight their growth rate. It defines for a specific forest site, which target stands are likely to be in the area and what kind of growth can be characterized. The results will be incorporated into the decision support system of the Agrárklima project after proper validation.

Czimber K. & Gálos B. 2016: A new decision support system to analyse the impacts of climate change on the Hungarian forestry and agricultural sectors. Scandinavian Journal of Forest Research 31: 664–673. DOI: 10.1080/02827581.2016.1212088

Czimber K. & Gálos B. 2016: A new decision support system to analyse the impacts of climate change on the Hungarian forestry and agricultural sectors. Scandinavian Journal of Forest Research 31: 664–673. DOI: 10.1080/02827581.2016.1212088

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